A functional vulnerability framework for biodiversity conservation

Setting appropriate conservation strategies in a multi-threat world is a challenging goal, especially because of natural complexity and budget limitations that prevent effective management of all ecosystems. Safeguarding the most threatened ecosystems requires accurate and integrative quantification of their vulnerability and their functioning, particularly the potential loss of species trait diversity which imperils their functioning. However, the magnitude of threats and associated biological responses both have high uncertainties. Additionally, a major difficulty is the recurrent lack of reference conditions for a fair and operational measurement of vulnerability. Here, we present a functional vulnerability framework that incorporates uncertainty and reference conditions into a generalizable tool. Through in silico simulations of disturbances, our framework allows us to quantify the vulnerability of communities to a wide range of threats. We demonstrate the relevance and operationality of our framework, and its global, scalable and quantitative comparability, through three case studies on marine fishes and mammals. We show that functional vulnerability has marked geographic and temporal patterns. We underline contrasting contributions of species richness and functional redundancy to the level of vulnerability among case studies, indicating that our integrative assessment can also identify the drivers of vulnerability in a world where uncertainty is omnipresent.


March 2021
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. This study introduces a novel framework for quantifying the vulnerability of biological communities by integrating the precautionary principle and ecological traits. There is indeed a need to prioritize the vulnerability of biological communities by considering the uncertainty in how human and environmental threats will change and how species will respond to them. Here we address these uncertainties by simulating community potential response to the widest range of threats. Our framework is applicable on a wide variety of data (abundance, presence/absence and probability of presence), spatial scale (from local to global) and time period (past, present, future). We applied our framework to three complementary cases: i) the vulnerability trend in North Sea fishes in the past four decades, ii) present-day spatial vulnerability of global marine mammals, and iii) projected vulnerability changes in global reef fishes under climate change scenario.
ii) We selected a representative data set currently used in macro-ecology at global scale. This sample encompass the presence absence of almost all marine mammals and the associated traits data for these species (case study 2). All the data are available at: https://figshare.com/articles/Input_data_for_Global_vulnerability_of_marine_mammals_to_global_warming_/11323304 and at: http://www.iucnredlist.org for species range maps.
iii) We compile a novel point record dataset on reef species occurrence and biomass available globally which provided a highly

March 2021
Sampling strategy

Data collection
Timing and spatial scale

Data exclusions
Reproducibility Randomization Blinding Did the study involve field work?
Yes No comprehensive spatial coverage of reef species distributions and thus was an appropriate choice for our study. Our final set comprised of 2,340 species at 496,309 unique georeferenced locations in 20,450 grid cells for analyses. We combined together presence/absence data obtained from OBIS and GBIF with in-hand reef fish underwater visual census observations. The in-hand data come from curated citizen science and professional surveys, namely the Reef Life Survey (RLS, Edgar & Stuart-Smith 2014), Socio-Ecological Reef Fish dataset (SERF, Mora et al. 2011, Cinner et al. 2018) and GASPAR project dataset (see supporting information in Barneche et al. 2018 for full sampling description). We used these data to derived habitat suitability models (case study 3) which can be made available from the authors upon reasonable request.
-Case study 2 (Marine mammals): We compiled geographic range maps from the IUCN database (http://www.iucnredlist.org) for all known marine mammal species (127 species). We deleted 5 species for which a major part of their range fall in freshwater environment (Sotalia Fluviatili, Inia geoffrensis Platanista gangetica, Pusa sibirica and Trichechus inunguis) and we finally retained 122 species. We then established a presence/absence matrix and derived SR by overlapping the geographic ranges and counting how many species occur in each grid cell (1°× 1°grid cells, 10,000 km²).
-Case study 1 (North Sea fishes): Abundance data were collected thanks to a many persons on oceanographic vessels that catched demersal fish communities with vertical a bottom trawl. After each haul, fishes were identify at species level and counted. Corresponding data were then uploaded on the DATRAS repository platform (https://datras.ices.dk/Data_products/Download/Download_Data_public.aspx). The first author (Arnaud Auber) extracted the data from the DATRAS database. Traits data were obtained through accumulation of observations and measurements during the last decades in various studies. These data were then combined and uploaded in the PANGAEA traits database thanks to Beukhof et al., 2019, at: https://doi.pangaea.de/10.1594/PANGAEA.900866.
-Case study 2 (Marine mammals): Considering the marine mammals data set, we downloaded the data from the IUCN database (http://www.iucnredlist.org). We conducted all the analyses by using the software R.
-Case study 3 (Reef fishes): Data were collected by professional and trained scuba divers recording fishes species identities and body size observed on underwater visual transects and fish body sizes (Reaf Life Survey, SERF, GASPAR), this is a well-established method for estimated fish community structure (see https://www.nature.com/articles/sdata20147; https://doi.org/10.1016/ j.biocon.2020.108855) -Case study 1 (North Sea fishes): Abundance data from 1983 to 2019 were selected at the North Sea scale. The starting year (1983) was selected based upon field experts who identified 1983 as the first year where the sampling protocol was considered as constant. Because the NS-IBTS survey is mainly funded to assess fish stocks, and thus to define annual fishing quotas, the sampling is done once a year (during the first quarter). The last year (2019) was selected as the last year of our temporal window because 2020 data were not still available when we extracted data. The spatial scale of the North Sea was used in order to have only one vulnerability value per year for the entire ecosystem.
-Case study 2 (Marine mammals): We used the marine mammals traits collated by Albouy et al 2017 and the IUCN shapefile for marine mammals (version 3) at global scale download from the IUCN website in February 2016. These data span on several decades with opportunistic observations to present day at a global scale.There is therfore no periodicity since the database only contains opportunistic observations.
-Case study 3 (Reef fishes): Reef fish occurrences were compiled by the Global Biodiversity Information Facility and Ocean Biodiversity Information System which provide access to unstructured biodiversity observations. These data span 1990 to present day at a global scale.
Sites for which species richness was inferior to 10 were not considered to prevent any over or underestimation of the functional vulnerability (case study 3).
R codes were computed in a way to be reproducible by users. Explanations are within the provided code No randomization was required because this is a study using observational data.
This is not an experimental design, so no blinding was required in this study.